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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-In this work, a precise method for end-to-end (E2E) latency measurement in satellite Internet protocol (SIP) network environment is proposed. Latency is considered a key parameter affecting the quality of service (QoS) and performance of communications. This is more pronounced in IP over Satellite. Metrics such as throughput and bandwidth performance of communications systems are dependent on latency, which also has a direct impact on other QoS metrics such as Internet packet transfer delay and delay variation or jitter. The upper limits of QoS objective performance metrics are defined by E2E latency for different QoS traffic classes in this environment. Therefore, there is a need to develop efficient methods for the accurate measurement of E2E latency in a SIP environment. Two case study scenarios were developed for satellite and hybrid networks to measure the latency in a SIP environment. Two Geostationary Satellite Network Services were used to compare the performance of the different scenarios and networks. The results demonstrate that at least 50% of the E2E latency is due to processing and transmitting IP packets over the satellite in both scenarios. Inconsistent latency behaviour was also observed from daily results at different times of the day, which may degrade performance of jitter sensitive applications.

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this paper, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can also degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Moreover, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.Index Terms-Data association, long-haul sensor networks, mean-square-error (MSE) and root-mean-square-error (RMSE) performance, message retransmission, prediction and retrodiction, state estimation and fusion.